Frame error concealment method and apparatus and decoding method and apparatus using the same

ABSTRACT

A frame error concealment method and apparatus and a decoding method and apparatus using the same. The frame error concealment method includes setting a concealment method to conceal an error based on one or more signal characteristics of an error frame having the error and concealing the error using the set concealment method.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §119(a) from KoreanPatent Application No. 10-2006-0118563, filed on Nov. 28, 2006, in theKorean Intellectual Property Office, the disclosure of which isincorporated herein in its entirety by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present general inventive concept relates to a method and apparatusto decode a voice signal or an audio signal, and more particularly, to aframe error concealment method and apparatus to conceal a frame errorgenerated in a decoded signal.

2. Description of the Related Art

When packets are lost or distorted during transmission of an encodedaudio signal over a wired/wireless network, an error may be generated ina frame of a decoded audio signal due to a transmission error. Unlessthe generated error is properly handled, the sound quality of the audiosignal degrades in the frame having the error. Moreover, since a decoderreconstructs a signal using prediction, the generated error continuouslypropagates to the next frame and the sound quality of the audio signalalso degrades in the next frame. Accordingly, it is important toefficiently conceal a frame error in order to prevent the sound qualityof a reconstructed audio signal from degrading.

There are various frame error concealment methods such as a mutingmethod, a repetition method, an interpolation method, an extrapolationmethod, and a regression analysis method. The muting method reducesvolume in a frame having an error, which will be referred to hereinafteras an error frame (EF), thereby alleviating the influence of the errorupon an output signal. The repetition method reconstructs a signal ofthe EF by repetitively reproducing a previous good frame (PGF) of theEF. The interpolation method predicts a parameter of the EF byinterpolating a parameter of the PGF and a parameter of a next goodframe (NGF). The extrapolation method obtains the parameter of the EF byextrapolating the parameter of the PGF. The regression analysis methodobtains the parameter of the EF by regressively analyzing the parameterof the PGF.

Conventionally, however, since the EF has been reconstructed for anytype of input signal using the same method, the frame error cannot beefficiently concealed, causing degradation in sound quality.

SUMMARY OF THE INVENTION

The present general inventive concept provides a frame error concealmentmethod and apparatus, in which a frame error is concealed using a methodthat is optimized for characteristics of a signal, thereby accuratelyreconstructing an error frame (EF).

The present general inventive concept also provides a decoding methodand apparatus, in which an EF is accurately reconstructed using a methodthat is optimized for the characteristics of a signal, therebyminimizing sound quality degradation caused by a frame error.

The present general inventive concept also provides a computer-readablemedium having recorded thereon a program to implement the frame errorconcealment method and the decoding method.

Additional aspects and utilities of the present general inventiveconcept will be set forth in part in the description which follows and,in part, will be obvious from the description, or may be learned bypractice of the general inventive concept.

The foregoing and/or other aspects and utilities of the present generalinventive concept may be achieved by providing a frame error concealmentmethod including setting a concealment method to conceal an error basedon one or more signal characteristics of an error frame having the errorand concealing the error using the set concealment method. The settingoperation may include setting a regression analysis method to concealthe error based on the one or more signal characteristics.

The setting operation may further include analyzing the one or moresignal characteristics and setting the regression analysis method basedon the analyzed one or more signal characteristics. The analyzingoperation may include analyzing the one or more signal characteristicsbased on information about a previous good frame.

The setting the regression analysis method operation may includeselecting at least one of a linear regression analysis and a non-linearregression analysis as the concealment method based on the one or moresignal characteristics, setting a number of previous good frames to bereferred to and to conceal the error using the set regression analysismethod based on the one or more signal characteristics, or setting aninterval to extract one or more parameters of a previous good frame tobe referred to and to conceal the error using the set regressionanalysis method based on the one or more signal characteristics.

The concealing operation may include predicting a parameter of the errorframe from one or more parameters of the previous good frame using theset regression analysis method. The concealing operation may furtherinclude deriving a regression analysis function to predict from the oneor more parameters of the previous good frame using the set regressionanalysis method and predicting the parameter of the error frame usingthe derived regression analysis function. The concealing operation mayfurther include adjusting the predicted parameter to a value included ina predetermined range when the predicted parameter falls outside thepredetermined range.

The setting operation may include setting an adjustment function toadjust the predicted parameter based on the one or more signalcharacteristics and the predicting the parameter operation may includeadjusting a coefficient of the derived function using the set adjustmentfunction and predicting the parameter of the error frame using thecoefficient-adjusted function. The function whose coefficient isadjusted using the set adjustment function may be a function to predicta parameter associated with energy information of the error frame.

The frame error concealment method may further include detecting theerror frame from a bitstream.

The foregoing and/or other aspects and utilities of the present generalinventive concept may also be achieved by providing a frame errorconcealment method including setting a concealment method to conceal anerror for an error layer including a position of the error and itsfollowing layer in an error frame having the error based on one or moresignal characteristics of the error frame and concealing the error usingthe set concealment method. The setting operation may include analyzingthe one or more signal characteristics based on information about aprevious good frame and information about a previous layer preceding theerror layer and setting the concealment method to conceal the errorbased on the analyzed one or more signal characteristics.

The concealing operation may include predicting one or more parametersof the error layer and its following layer from one or more parametersof the previous good frame and the previous layer using the setconcealment method.

The foregoing and/or other aspects and utilities of the present generalinventive concept may also be achieved by providing a frame errorconcealment apparatus including a concealment method setting unit to seta concealment method to conceal an error based on one or more signalcharacteristics of an error frame having the error and an errorconcealment unit to conceal the error using the set concealment method.The concealment method setting unit may set a regression analysis methodto conceal the error based on the one or more signal characteristics andthe error concealment unit may conceal the error using the setconcealment method.

The concealment method setting unit may include a signal characteristicanalysis unit to analyze the one or more signal characteristics and asetting unit to set the regression analysis method based on the analyzedone or more signal characteristics. The signal characteristic analysisunit may analyze the one or more signal characteristics based oninformation about a previous good frame.

The concealment method setting unit may select at least one of a linearregression analysis and a non-linear regression analysis as theconcealment method based on the one or more signal characteristics. Theconcealment method setting unit may set a number of previous good framesto be referred to and to conceal the error using the set regressionanalysis method based on the one or more signal characteristics. Theconcealment method setting unit may set an interval to extract one ormore parameters of a previous good frame to be referred to conceal theerror using the set regression analysis method based on the one or moresignal characteristics.

The error concealment unit may predict a parameter of the error framefrom one or more parameters of the previous good frame using the setregression analysis method.

The error concealment unit may include a function derivation unit toderive a regression analysis function to predict from the one or moreparameters of the previous good frame using the set regression analysismethod and a prediction unit to predict the parameter of the error frameusing the derived regression analysis function. The error concealmentunit may adjust the predicted parameter to a value included in apredetermined range when the predicted parameter falls outside thepredetermined range.

The concealment method setting unit may set an adjustment function toadjust the predicted parameter based on the one or more signalcharacteristics, and the error concealment unit may further include anadjustment unit that adjusts a coefficient of the derived function usingthe set adjustment function and the prediction unit predicts theparameter of the error frame using the coefficient-adjusted function.The function whose coefficient is adjusted using the set adjustmentfunction may be a function to predict a parameter associated with energyinformation of the error frame.

The frame error concealment apparatus may further include an errordetection unit to detect the error frame from a bitstream.

The foregoing and/or other aspects and utilities of the present generalinventive concept may also be achieved by providing a frame errorconcealment apparatus including a concealment method setting unit to seta concealment method to conceal an error for an error layer including aposition of the error and its following layer in an error frame havingthe error based on one or more signal characteristics of the error frameand an error concealment unit concealing the error using the setconcealment method. The concealment method setting unit may furtherinclude a signal characteristic analysis unit to analyze the one or moresignal characteristics based on information about a previous good frameand information about a previous layer preceding the error layer and asetting unit to set the concealment method to conceal the error based onthe analyzed one or more signal characteristics.

The error concealment unit may predict one or more parameters of theerror layer and its following layer from one or more parameters of theprevious good frame and the previous layer using the set concealmentmethod.

The foregoing and/or other aspects and utilities of the present generalinventive concept may also be achieved by providing a decoding methodincluding detecting an error frame having an error from a bitstream,decoding a frame having no error in the bitstream, setting a concealmentmethod to conceal the error based on one or more signal characteristicsof the error frame, and concealing the error using the set concealmentmethod.

The foregoing and/or other aspects and utilities of the present generalinventive concept may also be achieved by providing a decoding methodincluding detecting an error frame having an error from a bitstream anda position of the error in the error frame, decoding a frame having noerror in the bitstream and a previous layer preceding an error layerincluding the position of the error in the error frame, setting aconcealment method to conceal the error based on one or more signalcharacteristics of the error frame, and concealing the error using theset concealment method.

The foregoing and/or other aspects and utilities of the present generalinventive concept may also be achieved by providing a decoding apparatusincluding an error detection unit to detect an error frame having anerror from a bitstream, a decoding unit to decode a frame having noerror in the bitstream, and an error concealment unit to set aconcealment method to conceal the error based on one or more signalcharacteristics of the error frame and to conceal the error using theset concealment method.

The foregoing and/or other aspects and utilities of the present generalinventive concept may also be achieved by providing a decoding apparatusincluding an error detection unit to detect an error frame having anerror from a bitstream and a position of the error in the error frame, adecoding unit to decode a frame having no error in the bitstream and aprevious layer preceding an error layer including the position of theerror in the error frame, and an error concealment unit to set aconcealment method to conceal the error based on one or more signalcharacteristics of the error frame and concealing the error using theset concealment method.

The foregoing and/or other aspects and utilities of the present generalinventive concept may also be achieved by providing a computer-readablemedium having recorded thereon a program to implement the frame errorconcealment method setting a concealment method to conceal an errorbased on one or more signal characteristics of an error frame having theerror, and concealing the error using the set concealment method.

The foregoing and/or other aspects and utilities of the present generalinventive concept may also be achieved by providing a method ofconcealing an error of an audio signal transmitted in a plurality offrames, the method including detecting an error frame having an error inone or more of the plurality of frames transmitting the audio signal,setting a concealment process to conceal the error based on one or moresignal characteristics of the error frame, and concealing the errorusing the set concealment process.

The foregoing and/or other aspects and utilities of the present generalinventive concept may also be achieved by providing a method ofreconstructing an audio signal, the method including determining whetheran error frame having an error exists and, if so, analyzing one or moresignal characteristics of the error frame based on information about aprevious frame not having an error, setting a regression analysisprocess based on the analyzed one or more signal characteristics,reconstructing a spectrum parameter of the error frame from one or morespectrum parameters of the previous frame using the regression analysisprocess, and reconstructing the audio signal using an audio signal ofthe error frame and the reconstructed spectrum parameter.

The foregoing and/or other aspects and utilities of the present generalinventive concept may also be achieved by providing a method ofreconstructing an audio signal, the method including determining whetheran error frame having an error exists and, if so, analyzing one or moresignal characteristics of the error frame based on information about aprevious frame not having an error, setting a regression analysisprocess based on the analyzed one or more signal characteristics,reconstructing a gain parameter of the error frame from one or more gainparameters of the previous frame not having the error using theregression analysis process, reconstructing an excitation signal of theerror frame based on the reconstructed gain parameter, reconstructing aline spectral pair (LSP) parameter of the error frame from an LSPparameter of the previous frame using the regression analysis process,and reconstructing the audio signal using an audio signal of the errorframe, the reconstructed LSP parameter and the reconstructed excitationsignal.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects and utilities of the present generalinventive concept will become apparent and more readily appreciated fromthe following description of the embodiments, taken in conjunction withthe accompanying drawings of which:

FIG. 1A is a block diagram of an audio decoding apparatus illustrating aframe error concealment apparatus according to an embodiment of thepresent general inventive concept;

FIG. 1B is a detailed block diagram of a frame error concealment unitillustrated in FIG. 1A;

FIG. 2 is a block diagram of a voice decoding apparatus illustrating aframe error concealment apparatus according to an embodiment of thepresent general inventive concept;

FIG. 3A is a detailed block diagram illustrating an excitation signalreconstruction unit illustrated in FIG. 2;

FIG. 3B is a detailed block diagram illustrating a line spectral pair(LSP) reconstruction unit illustrated in FIG. 2;

FIG. 4A is a graph illustrating an exemplary function derived usinglinear regression analysis;

FIG. 4B is a graph illustrating an exemplary function derived usingnon-linear regression analysis;

FIG. 5 is a block diagram of an audio decoding apparatus illustrating aframe error concealment apparatus according to another embodiment of thepresent general inventive concept;

FIG. 6 is a block diagram of an audio decoding apparatus illustrating aframe error concealment apparatus according to another embodiment of thepresent general inventive concept;

FIG. 7 is a flowchart of a voice decoding method illustrating a frameerror concealment method according to an embodiment of the presentgeneral inventive concept;

FIG. 8 is a detailed flowchart illustrating signal characteristicanalysis and concealment method setting illustrated in FIG. 7;

FIG. 9 is a detailed flowchart illustrating excitation signalreconstruction illustrated in FIG. 7;

FIG. 10 is a detailed flowchart illustrating LSP parameterreconstruction illustrated in FIG. 7;

FIG. 11 is a flowchart illustrating an audio decoding method using aframe error concealment method according to an embodiment of the presentgeneral inventive concept;

FIG. 12 is a detailed flowchart illustrating signal characteristicanalysis and concealment method setting illustrated in FIG. 11; and

FIG. 13 is a flowchart illustrating an audio decoding method using aframe error concealment method according to another embodiment of thepresent general inventive concept.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Reference will now be made in detail to the embodiments of the presentgeneral inventive concept, examples of which are illustrated in theaccompanying drawings, wherein like reference numerals refer to the likeelements throughout. The embodiments are described below in order toexplain the present general inventive concept by referring to thefigures.

FIG. 1A is a block diagram illustrating an audio decoding apparatus 100including a frame error concealment apparatus according to an embodimentof the present general inventive concept, and FIG. 1B is a detailedblock diagram illustrating a frame error concealment unit 130illustrated in FIG. 1A. Referring to FIGS. 1A and 1B, the audio decodingapparatus 100 includes an error detection unit 110, a decoding unit 120,and the frame error concealment unit 130. The frame error concealmentunit 130 includes a concealment method setting unit 140 and an errorconcealment unit 150. The concealment method setting unit 140 includes asignal characteristic analysis unit 142 and a setting unit 144.

The error detection unit 110 detects a frame having an error, which willbe referred to hereinafter as an error frame (EF), from a transmittedbitstream, and informs the frame error concealment unit 130 that the EFis detected. The frame may be a single frame or a sub frame included ina single frame.

The decoding unit 120 decodes a good frame (GF) having no error in thebitstream. The decoding unit 120 may be implemented by using a voicecodec such as International Telecommunication Union-TelecommunicationStandardization Sector (ITU-T) G.729 or an audio codec such as MovingPicture Experts Group (MPEG)-2/4 Advanced Audio Coding (AAC), and MovingPicture Experts Group-Bit Sliced Arithmetic Coding (MPEG-BSAC).

The signal characteristic analysis unit 142 analyzes the signalcharacteristics of the EF detected by the error detection unit 110 andtransmits the analyzed signal characteristics to the setting unit 144.The setting unit 144 sets a concealment method to conceal a frame errorbased on the transmitted signal characteristics. The error concealmentunit 150 conceals the frame error using the set concealment method.

Hereinafter, the operation of a frame error concealment apparatusaccording to an embodiment of the present general inventive concept andthe operation of a decoding apparatus using the frame error concealmentapparatus will be described in detail where a signal to be reconstructedis a voice signal and where a signal to be reconstructed is an audiosignal.

FIG. 2 is a block diagram illustrating a voice decoding apparatus 200,including a frame error concealment apparatus (unit) 230 according to anembodiment of the present general inventive concept. The voice decodingapparatus 200 includes an error detection unit 210, a decoding unit 220,and the frame error concealment unit 230. The decoding unit 220 includesan excitation signal decoding unit 240, a line spectral pair (LSP)decoding unit 250, an LSP/linear prediction coefficient (LPC) conversionunit 260, and a synthesis filter 270. The frame error concealment unit230 includes a concealment method setting unit 280 and an errorconcealment unit 290. The concealment method setting unit 280 includes asignal characteristic analysis unit 282 and a setting unit 284. Theerror concealment unit 290 includes an excitation signal reconstructionunit 292 and an LSP reconstruction unit 294.

Hereinafter, the operation of the voice decoding apparatus 200illustrated in FIG. 2 will be described.

The error detection unit 210 detects an EF from a bitstream andtransmits the EF to the frame error concealment unit 230, and transmitsa GF to the decoding unit 220.

The decoding unit 220 decodes parameters of the transmitted GF andreconstructs a voice signal using the decoded parameters. When a codeexcited linear prediction algorithm based on a voice utterance model isused in an embodiment of the present general inventive concept, thedecoding unit 220 reconstructs an LSP parameter having 10 roots obtainedby analyzing the frequency characteristics of the voice signal andparameters to synthesize an excitation signal and synthesizes the voicesignal using the reconstructed parameters. The parameters to synthesizethe excitation signal may include a pitch period, a pulse sound source(the position of a pulse), a gain gc for a pulse sound source signal,and a gain gp for an adaptive codebook sound source signal. Theexcitation signal decoding unit 240 decodes the parameters to synthesizethe excitation signal and synthesizes excitation signal using thedecoded parameters.

The frame error concealment unit 230 sets a concealment method toconceal an EF according to signal characteristics and conceals a frameerror using the set concealment method.

In an embodiment of the present general inventive concept, theconcealment method setting unit 280 analyzes the signal characteristicsand sets a regression analysis method considering the analyzed signalcharacteristics, and the error concealment unit 290 conceals a frameerror using the set regression analysis method. For purposes of clarity,frame error concealment performed by the error concealment unit 290using the regression analysis method will be described prior to adescription regarding setting of the regression analysis method toconceal frame error.

As discussed previously, the parameters to synthesize the excitationsignal may include a pitch period, a fixed codebook index, an adaptivecodebook gain gp, and a fixed codebook gain gc. The excitation signalreconstruction unit 292 predicts a parameter to synthesize an excitationsignal of an EF from a parameter to synthesize an excitation signal of aprevious good frame (PGF) and synthesizes the excitation signal of theEF using the predicted parameter, thereby reconstructing the excitationsignal. The parameter to synthesize the excitation signal of the PGF isreconstructed by the excitation signal decoding unit 240 illustrated inFIG. 2 and may be stored in a predetermined buffer (not illustrated) foruse in reconstruction of the EF. When an EF is detected, the excitationsignal reconstruction unit 292 may read the parameter corresponding tothe PGF from the predetermined buffer and reconstruct the excitationsignal of the EF.

Hereinafter, the operation of the excitation signal reconstruction unit292 will be described with reference to FIG. 3A. FIG. 3A is a detailedblock diagram illustrating the excitation signal reconstruction unit 292illustrated in FIG. 2. The excitation signal reconstruction unit 292includes a first function derivation unit 300, a gain adjustment unit310, a first prediction unit 320, a first post-processing unit 330, andan excitation signal synthesis unit 340.

The first function derivation unit 300 derives a function from gainparameters gp and gc of the PGF by using regression analysis. Thederived function is a linear function or a non-linear function. Thenon-linear function may be an exponential function, a logarithmicfunction, or a power function. When the concealment method setting unit280 (FIG. 2) sets linear regression analysis to predict a parameter ofan EF, a linear function may be derived. When the concealment methodsetting unit 280 sets non-linear regression analysis to predict theparameter of the EF, the non-linear function may be derived. A singleframe is composed of a plurality of sub frames and a function for a gainis derived from a gain parameter of each of the sub frames by usingregression analysis.

FIGS. 4A and 4B are graphs illustrating exemplary functions derivedusing linear regression analysis and non-linear regression analysis withrespect to parameters of a PGF or a sub frame. In FIG. 4A, a linearfunction is derived from gain parameters (x1, x2, through to x8) of thePGF. In FIG. 4B, a non-linear function is derived from the gainparameters (x1, x2, through to x8) of the PGF. In FIGS. 4A and 4B, a andb are constants obtained by regression analysis.

Referring to FIG. 3A, the gain adjustment unit 310 adjusts a coefficientof the derived function according to a voiced level of the PGF. Forexample, when the first function derivation unit 300 derives a linearfunction such as Equation 1, the gain adjustment unit 310 adjusts acoefficient of the derived non-linear function as in Equation 2.

x(i)=ax+b   (1)

a′=f(g _(p)(n),g _(p)(n−1), . . . , g _(p)(n−K))a   (2),

where a and b are constants obtained by regression analysis, and wheref( ) is a gain adjustment function that reduces a gradient a′ when avoiced level is high. g_(p)(n),g_(p)(n−1), . . . , g_(p)(n−K) areadaptive codebook gain parameters of a PGF. Since the same signal isrepeated for a predetermined amount of time in voiced sound, significantreduction in an amplitude of a voice signal can be adaptively preventedby reducing the gradient a′ when a voiced level is high. The gainadjustment unit 310 compensates for inaccurate prediction of a gain ofthe EF by regression analysis. High correlation between a current signaland its previous signal in voiced sound originates from energydistribution of a voice signal and a gain parameter has correlation withthe energy of the voice signal. Accordingly, gain adjustment is appliedto the gain parameter.

Referring to FIGS. 2 and 3A, the first prediction unit 320 predicts aparameter of the EF using the function whose coefficient is adjusted bythe gain adjustment unit 310. When the concealment method setting unit280 sets linear regression analysis as a concealment method to predictthe parameter of the EF, a gain parameter xPL of the EF is predictedusing a linear function, for example, like in FIG. 4A. When theconcealment method setting unit 280 sets non-linear regression analysisas a concealment method to predict the parameter of the EF, a gainparameter xPN of the EF is predicted using a non-linear function. Whenthe first function derivation unit 300 derives a linear function such asEquation 1 and the gain adjustment unit 310 adjusts the coefficient ofthe derived function, for example, like in Equation 2, the firstprediction unit 320 may predict a gain parameter, {circumflex over(x)}(i), using the coefficient-adjusted function, as follows:

{circumflex over (x)}(i)=a′i+b   (3)

where a′ is an adjusted coefficient and b is a constant obtained byregression analysis.

The first post-processing unit 330 optimizes the predicted gainparameter. For example, an upper limit and a lower limit are preset, andwhen the predicted gain parameter is greater than the upper limit orless than the lower limit, it is adjusted to fall within a predeterminedrange defined by the upper limit and the lower limit, thereby preventingthe gain parameter from being predicted as being an improbable value.

The excitation signal synthesis unit 340 synthesizes the excitationsignal of the EF by referring to the gain parameters gp and gc of theEF, which are predicted by the gain adjustment unit 314 or the firstprediction unit 320. In an embodiment of the present general inventiveconcept, a pitch period of a fixed codebook index of a previous frame oran arbitrarily generated value may be used as a pitch period or a fixedcodebook index required to synthesize the excitation signal of the EF.More details can be found in a unique function to conceal a frame errorin ITU-T G.729 that is incorporated herein in its entirety by reference.

The excitation signal synthesized by the excitation signal synthesisunit 340 is a reconstructed excitation signal for the EF and is outputto the synthesis filter 270 illustrated in FIG. 2.

Hereinafter, the operation of the LSP reconstruction unit 294 will bedescribed with reference to FIG. 3B. FIG. 3B is a detailed block diagramillustrating the LSP reconstruction unit 294 illustrated in FIG. 2. TheLSP reconstruction unit 294 includes an LSP/spectrum conversion unit350, a second function derivation unit 360, a second prediction unit370, a second post-processing unit 380, and a spectrum/LSP conversionunit 390. The LSP reconstruction unit 294 reconstructs an LSP parameterof the EF from an LSP parameter of the PGF using regression analysis.

Referring to FIGS. 2 and 3B, the LSP parameter of the PGF isreconstructed by the LSP decoding unit 250 and may be stored in apredetermined buffer (not illustrated) for use in reconstruction of theEF, like in the excitation signal reconstruction unit 292. When the EFis detected, the LSP reconstruction unit 294 may read a parameter of thePGF stored in the predetermined buffer and reconstruct the LSP parameterof the EF.

The LSP/spectrum conversion unit 350 converts the LSP parameter of thePGF, which has 10 roots, into a spectral domain to obtain a spectrumparameter.

The second function derivation unit 360 derives a function from thespectrum parameter of the PGF using regression analysis. Like in thefirst function derivation unit 300, the derived function may be a linearfunction or a non-linear function according to the setting of theconcealment method setting unit 280. FIGS. 4A and 4B are graphsillustrating exemplary functions derived using regression analysis andnon-linear regression analysis with respect to parameters of a PGF or asub frame. In FIG. 4A, a linear function x(i) =ax+b is derived fromspectrum parameters (x1, x2, through to x8) of the PGF. In FIG. 4B, anon-linear function x(i)=a is derived from spectrum gain parameters (x1,x2, through to x8) of the PGF. In FIGS. 4A and 4B, a and b are constantsobtained by regression analysis.

The second prediction unit 370 predicts a spectrum parameter of the EFusing the derived function. A spectrum parameter xPL of the EF ispredicted using a linear function in FIG. 4A and a spectrum parameterxPN of the EF is predicted using a non-linear function in FIG. 4B.

The second post-processing unit 380 optimizes the predicted LSPparameter. For example, an upper limit and a lower limit are preset, andwhen the predicted gain parameter is greater than the upper limit orless than the lower limit, it is adjusted to fall within a predeterminedrange defined by the upper limit and the lower limit, thereby preventingthe gain parameter from being predicted as being an improbable value.

The spectrum/LSP conversion unit 390 converts the predicted spectrumparameter to the LSP parameter in order to reconstruct the LSP parameterof the EF. The reconstructed LSP parameter is output to the LSP/LPCconversion unit 260 illustrated in FIG. 2.

As discussed above, the LSP parameter of the EF, which is reconstructedby the LSP reconstruction unit 294, is output to the LSP/LPC conversionunit 260 and the excitation signal of the EF, which is reconstructed bythe excitation signal reconstruction unit 292, is output to thesynthesis filter 270. Thus, the decoding unit 220 decodes a signal ofthe EF using the LPS parameter and the excitation signal that arereconstructed by the frame error concealment unit 230 in the same manneras when it decodes a signal of a GF. By doing so, the error of the EFcan be concealed.

Hereinafter, a process of setting a regression analysis method based onsignal characteristics in the concealment method setting unit 280 willbe described. Referring to FIG. 2, the concealment method setting unit280 includes the signal characteristic analysis unit 282 and the settingunit 284.

The signal characteristic analysis unit 282 analyzes the signalcharacteristics of the EF based on information about the PGF. Theanalyzed signal characteristics are used for the setting unit 234 to seta concealment method to conceal a frame error. According to anembodiment of the present general inventive concept, the signalcharacteristic analysis unit 282 analyzes signal characteristics basedon class information of the PGF. The class information is obtained byclassifying a voice signal according to the characteristics of afrequency shift and may be one of Voiced, Unvoiced, Transition, Onset,Offset, Silence, and Background Noise.

When the signal characteristic analysis unit 282 analyzes signalcharacteristics based on the class information of the PGF, it mayperform analysis according to the class information as follows. Since aconstant frequency continues for a predetermined long period of time invoiced sound, when the class information of the PGF is Voiced,correlation between the current signal and its previous signal is high.In contrast, correlation between the current signal and its previoussignal is low in unvoiced sound or background noise. Accordingly, signalcharacteristics such as whether parameters representing thecharacteristics of voice changes linearly or non-linearly or having highcorrelation with those of a previous frame can be analyzed according towhether the voice signal is in a voiced, unvoiced, or transition state.

According to another embodiment of the present general inventiveconcept, the signal characteristic analysis unit 282 may analyze signalcharacteristics based on energy information of the PGF. Accordingly, thesignal characteristic analysis unit 282 may analyze signal correlationbetween the current frame and its previous frame by analyzing whether asignal is stable or unstable according to a change of energy. The signalcharacteristics can also be analyzed according to various aspects of thepresent general inventive concept in addition to the above-describedembodiments of the present general inventive concept.

The setting unit 284 sets a regression analysis method to conceal aframe error based on the signal characteristics analyzed by the signalcharacteristic analysis unit 282. For example, the setting unit 284 mayset whether to use linear regression analysis or non-linear regressionanalysis or set the number of PGFs to be referred to for regressionanalysis. When the gain parameter of the EF is predicted usingregression analysis, the setting unit 284 may also set an adjustmentfunction to adjust predicted parameters.

Hereinafter, setting of the concealment method setting unit 280according to an embodiment of the present general inventive concept willbe described with reference to FIGS. 2 and 8. FIG. 8 is a detailedflowchart illustrating signal characteristic analysis and concealmentmethod setting illustrated in FIG. 7.

In operation 800, the signal characteristic analysis unit 282 analyzessignal characteristics based on the class information or energyinformation of the PGF. The signal characteristic analysis unit 282analyzes whether the current signal is voiced sound, unvoiced sound,silence, in a transition state, in an onset state, in an offset state,or background noise.

In operation 810, the setting unit 284 determines whether the currentsignal is silence based on the analyzed signal characteristics. If it isdetermined that the current signal is silence, the setting unit 284performs setting to use linear regression analysis to reconstruct aparameter of the EF in operation 820 and to perform regression analysisby referring to M PGFs in operation 830. If it is determined that thecurrent signal is not silence in operation 810, the setting unit 284performs setting to use non-linear regression analysis to reconstruct aparameter of the EF in operation 840. The setting unit 284 determineswhether the current signal is voiced sound in operation 850. If it isdetermined that the current signal is voiced sound, the setting unit 284performs setting to perform regression analysis by referring toparameters of M PGFs in operation 860. If it is determined that thecurrent signal is not voiced sound in operation 850, the setting unit284 performs setting to perform regression analysis by referring to NPGFs in operation 870. For example, M may be an integer that is greaterthan N. Since voiced sound has high correlation with a previous signal,it is desirable to refer to a longer interval of the previous signalthan with unvoiced sound in order to obtain an accurate and naturalsignal. However, there may be various methods to analyze signalcharacteristics and setting frame error concealment methods based on theanalyzed signal characteristics without being limited to the abovedescription and methods that can be easily construed from the presentgeneral inventive concept by those of ordinary skill in the art that arewithin the scope of the present general inventive concept.

The error concealment unit 290 conceals a frame error according to aconcealment method set by the concealment method setting unit 280. Theoperation of the error concealment unit 290 has already been describedabove.

An audio decoding apparatus 500 including a frame error concealmentapparatus (unit) 530 according to another embodiment of the presentgeneral inventive concept will now be described with reference to FIG.5. Referring to FIG. 5, the audio decoding apparatus 500 includes anerror detection unit 510, a decoding unit 520, and the frame errorconcealment unit 530. The decoding unit 520 includes a lossless decodingunit 540, a dequantization unit 550, and a filter bank 560. The frameerror concealment unit 530 includes a concealment method setting unit570 and an error concealment unit 580. The concealment method settingunit 570 includes a signal characteristic analysis unit 572 and asetting unit 574.

In general, an audio encoding apparatus according to MPEG-2/4 AACperforms modulated discrete cosine transformation (MDCT) on an audiosignal in order to extract a spectrum parameter for frequency componentsof the audio signal. The audio encoding apparatus performs losslessencoding on the extracted spectrum parameter in order to generate abitstream and transmits the generated bitstream to the audio decodingapparatus 500.

Like in FIG. 2, the error detection unit 510 detects an EF from thetransmitted bitstream and provides the detected EF to the frame errorconcealment unit 530, and provides a GF to the decoding unit 520.

The decoding unit 520 reconstructs a spectrum parameter of the providedGF and synthesizes an audio signal of the GF from the reconstructedspectrum parameter. The lossless decoding unit 540 performs losslessdecoding on a bitstream corresponding to the GF using a Huffmanalgorithm and the dequantization unit 550 dequantizes the GF, therebyreconstructing the spectrum parameter. The filter bank 560 performsinverse MDCT on the reconstructed spectrum and reconstructs an audiosignal of a time domain.

The signal characteristic analysis unit 572 analyzes signalcharacteristics based on information about a PGF of the EF. Theinformation about the PGF for the analysis may include attack signalinformation, window information, and energy information.

The attack signal information may include information about attack soundincluded in a frame. The attack sound indicates sound having a strongbass band included in an audio signal and the attack sound is notperiodic. Thus, when the attack sound is included in the audio signal,the signal characteristic analysis unit 560 may analyze that thecorrelation between the current signal and its previous signal is nothigh.

The window information may include information about the size or shapeof a window that has been used when the audio encoding apparatusperforms MDCT with respect to pulse code modulation (PCM) samples thatare obtained by sampling and quantizing the audio signal. In order toextract the spectrum parameter, the audio encoding apparatus performsMDCT with respect to a time-domain signal using a large window for astatic signal that changes little in terms of frequency spectrum, whileperforming MDCT using a small window for a dynamic signal that changesby a large amount in terms of frequency spectrum. Thus, when the size ofa window used by the audio encoding apparatus is large, the signalcharacteristic analysis unit 572 may analyze that the current signal isstatic and thus the correlation between the current signal and itsprevious signal is high. However, when the size of the window is small,the signal characteristic analysis unit 572 may analyze that thecorrelation between the current signal and its previous signal is low oran interval of the previous signal, which has high correlation with thecurrent signal, is short.

The energy information may include energy information of a frame or asub frame. The signal characteristic analysis unit 572 recognizes achange of the energy of the previous signal from energy information ofthe PGF and analyzes whether the current signal is static or dynamicaccording to the change of the energy. When the energy of the previoussignal changes little or is stable, the signal characteristic analysisunit 572 may analyze that the current signal is a static signal havinghigh correlation with the previous signal. When the energy of theprevious signal changes by a large amount or its change is notpredictable, the signal characteristic analysis unit 572 may analyzethat the current signal is a dynamic signal having low correlation withthe previous signal. Signal characteristic analysis may be possibleaccording to various aspects of the present general inventive conceptwithout being limited to the above-described embodiments of the presentgeneral inventive concept.

According to another embodiment of the present general inventiveconcept, the signal characteristic analysis unit 572 may analyze whetherthe current signal is static or dynamic by considering at least one ofattack signal information, window information, and energy informationand provide the analyzed signal characteristics to the setting unit 574.

The setting unit 574 sets a regression analysis method to conceal aframe error based on the signal characteristics provided by the signalcharacteristic analysis unit 572. Like the setting unit 284 illustratedin FIG. 3, the setting unit 574 may set whether to use linear regressionanalysis or non-linear regression analysis or the number of PGFs to bereferred to for regression analysis. When a spectrum parameter of the EFis predicted using regression analysis, an adjustment function to adjustthe predicted spectrum parameter may also be set.

Hereinafter, setting of the concealment method setting unit 570according to another embodiment of the present general inventive conceptwill be described with reference to FIGS. 5 and 12. FIG. 12 is adetailed flowchart illustrating signal characteristic analysis andconcealment method setting illustrated in FIG. 11.

In operation 1200, the signal characteristic analysis unit 572 analyzessignal characteristics based on window information and energyinformation of a PGF.

In operation 1210, the setting unit 574 determines whether the currentsignal is static based on the analyzed signal characteristics. When itis determined that the current signal is static, the setting unit 574performs setting to reconstruct a parameter of an EF using linearregression analysis in operation 1220 and to perform regression analysisusing K PGFs in operation 1230. When it is determined that the currentsignal is not static in operation 1210, the setting unit 574 performssetting to reconstruct the parameter of the EF using non-linearregression analysis in operation 1240 and to perform regression analysisusing L PGFs in operation 1250. Accordingly, K may be an integer that isgreater than L. Since a static audio signal has high correlation withits previous signal, it is desirable to refer to a longer interval ofthe previous signal than a dynamic audio signal in order to obtain anaccurate and natural signal. In contrast, since the dynamic audio signalhas low correlation with its previous signal, it is desirable to referto a shorter interval of the previous signal than the static audiosignal. However, there may be various methods to analyze signalcharacteristics and setting frame error concealment methods based on theanalyzed signal characteristics without being limited to the abovedescription and methods that can be easily construed from the presentgeneral inventive concept by those of ordinary skill in the art that arewithin the scope of the present general inventive concept.

The error concealment unit 580 conceals a frame error according to aregression analysis method that is set by the setting unit 574. Like theerror concealment unit 290 illustrated in FIG. 2, the error concealmentunit 580 may conceal a frame error by reconstructing a spectrumparameter of the EF from spectrum parameters of the PGF using regressionanalysis. The reconstructed spectrum parameter is provided to the filterbank 560 of the decoding unit 520 and the filter bank 560 reconstructsan audio signal of the EF using the reconstructed spectrum parameter ofthe EF like when it reconstructs the audio signal of the EF usingspectrum parameters of a GF. However, when MDCT parameters of the PGFare decoded for each sub band of a frequency band, the error concealmentunit 580 may also reconstruct the parameter of the EF from theparameters of the PGF for each sub band.

Hereinafter, the operation of the error concealment unit 580 accordingto another embodiment of the present general inventive concept will bedescribed with reference to FIG. 5. The error concealment unit 580illustrated in FIG. 5 predicts the spectrum parameter of the EF from thespectrum parameters of the PGF using regression analysis in a similarmanner to a process in which the excitation signal reconstruction unit292 of FIG. 3A and the LSP reconstruction unit 294 of FIG. 3B predictthe spectrum parameter of the EF from the spectrum parameters of the PGFusing regression analysis and thus a detailed description of theoperation of the error concealment unit 580 will be omitted.

The operation of an audio decoding apparatus 600 including a frame errorconcealment apparatus (unit) 630 according to another embodiment of thepresent general inventive concept will be described with reference toFIG. 6. According to another embodiment of the present general inventiveconcept, the audio decoding apparatus 600 decodes a bitstream composedof a plurality of layers. Like in Bit Sliced Arithmetic Coding (BSAC),when a bitstream is composed of a plurality of layers and a frequencyband is assigned to each of the layers, good layers of the bitstream,which precede a layer having an error (which will be referred to as anerror layer), can be reconstructed. Thus, according to anotherembodiment of the present general inventive concept, even when an erroris generated in a frame, information reconstructed prior to thegeneration of the error can be detected, thereby improving soundquality.

FIG. 6 is a block diagram illustrating the audio decoding apparatus 600including the frame error concealment apparatus 630 according to anotherembodiment of the present general inventive concept. The audio decodingapparatus 600 includes an error detection unit 610, a decoding unit 620,and a frame error concealment unit 630. The decoding unit 620 includes alossless decoding unit 640, a dequantization unit 650, and a filter bank660. The frame error concealment unit 630 includes a concealment methodsetting unit 670 and an error concealment unit 680. The concealmentmethod setting unit 670 includes a signal characteristic analysis unit672 and a setting unit 674.

Since the structure of the audio decoding apparatus 600 illustrated inFIG. 6 is similar to the structure of the audio decoding apparatus 500illustrated in FIG. 5, the following description will be focused on thedifference between the audio decoding apparatus 600 and the audiodecoding apparatus 500.

The error detection unit 610 detects an EF from a transmitted bitstreamand detects the position of an error in the EF. This is because when thebitstream has a layered structure, the decoding unit 620 can normallydecode a previous good layer (PGL) that precedes the position of theerror.

The decoding unit 620 reconstructs spectrum parameters of a given GF ora good layer and reconstructs an audio signal of a frame from thereconstructed spectrum parameters. The lossless decoding unit 640performs lossless decoding with respect to a bitstream corresponding toa GF or a good layer of an EF using arithmetic decoding and thedequantization unit 650 dequantizes the bitstream, therebyreconstructing a spectrum parameter. The filter bank 660 performsinverse MDCT on the reconstructed spectrum parameter, therebyreconstructing an audio signal of a time domain.

The frame error concealment unit 630 receives a layer including theposition of an error and its following layer included in the EF from theerror detection unit 610 and conceals the error.

According to another embodiment of the present general inventiveconcept, the signal characteristic analysis unit 660 analyzes signalcharacteristics based on information about a PGL preceding a layerincluding the position of an error as well as information about a PGF.The information about the PGF or the PGL may include attack signalinformation, window information, and energy information previouslydescribed with reference to FIG. 5.

The setting unit 670 sets a regression analysis method to conceal aframe error based on the signal characteristics analyzed by the signalcharacteristic analysis unit 660. The detailed operation of the settingunit 670 is similar to that of the setting unit 570 illustrated in FIG.5 and thus will not be described.

The error concealment unit 680 conceals the error of a frame accordingto the regression analysis method set by the setting unit 670. The errorconcealment unit 680 reconstructs spectrum parameters of an error layerand its following layer in the EF using spectrum parameters of a PGL ofthe EF as well as spectrum parameters of a PGF. A detailed method topredict spectrum parameters of the current signal from spectrumparameters of a previous signal using regression analysis is similar tothat of an LSP reconstruction unit 294 or an excitation signalreconstruction unit 292 illustrated in FIG. 3A and thus will not bedescribed.

The spectrum parameters of the error layer and its following layer,which are reconstructed by the error concealment unit 680, aretransmitted to the filter bank 650 of the decoding unit 660.

The filter bank 650 reconstructs an audio signal of the EF usingspectrum parameters of a PGL of the EF, which have been normally decodedby the decoding unit 620, and the spectrum parameters of the error layerand its following layer, which have been reconstructed by the frameerror concealment unit 630.

Like in BSAC, when a bitstream is composed of a plurality of layers,good layers of the bitstream, which precede an error layer, can benormally reconstructed by the decoding unit 620. Thus, the spectrumparameters of good layers preceding the error layer are reconstructed bythe decoding unit 620 and the spectrum parameters of the error layer andits following layer are reconstructed by the frame error concealmentunit 630, thereby accurately reconstructing an audio signal of the EF.

Hereinafter, a voice decoding method using frame error concealmentaccording to an embodiment of the present general inventive concept willbe described with reference to FIG. 7. FIG. 7 is a flowchartillustrating a voice decoding method using a frame error concealmentmethod according to an embodiment of the present general inventiveconcept.

In operation 700, it is determined whether a frame of a bitstream has anerror. If it is determined that no error is generated in operation 700,a voice signal of a GF having no error is reconstructed in operation710. If it is determined that an error is generated in operation 700,signal characteristics are analyzed based on information about a PGF inoperation 720. A regression analysis method to conceal the error is setbased on the analyzed signal characteristics in operation 730. Inoperation 740, a gain parameter of the EF is reconstructed using gainparameters gp and gc of the PGF using the set regression analysismethod. In operation 745, an excitation signal of the EF isreconstructed using the reconstructed gain parameter. In operation 750,an LSP parameter of the EF is reconstructed from an LSP parameter of thePGF using the set concealment method. In operation 760, a voice signalof the EF is reconstructed using the reconstructed excitation signal andthe reconstructed LSP parameter.

Hereinafter, signal characteristic analysis (operation 720) andconcealment method setting (operation 730) illustrated in FIG. 7 will bedescribed in detail with reference to FIG. 8. FIG. 8 is a detailedflowchart illustrating operation 720 and operation 730 illustrated inFIG. 7.

In operation 800, signal characteristics are analyzed based oninformation about the PGF. In operation 810, it is determined whetherthe current signal is silence based on the analyzed signalcharacteristics. When it is determined that the current signal issilence in operation 810, setting is performed to reconstruct aparameter of the EF using linear regression analysis in operation 820and to perform regression analysis by referring to M PGFs in operation830. When it is determined that the current signal is not silence inoperation 810, setting is performed to reconstruct the parameter of theEF using non-linear regression analysis in operation 840. In operation850, it is determined whether the current signal is voiced sound. Whenit is determined that the current signal is voiced sound, setting isperformed to perform regression analysis by referring to parameters of MPGFs in operation 860. When it is determined that the current signal isnot voiced sound in operation 850, setting is performed to performregression analysis by referring to N PGFs in operation 870. Here, M maybe an integer that is greater than N. Since voiced sound has highcorrelation with a previous signal, it is desirable to refer to a longerinterval of the previous signal than with unvoiced sound in order toobtain an accurate and natural signal. In contrast, since unvoiced soundhas low correlation with a previous signal, it is desirable to refer toa shorter interval of the previous signal than in the case of voicedsound.

Hereinafter, excitation signal reconstruction (operation 740)illustrated in FIG. 7 will be described in detail with reference to FIG.9. FIG. 9 is a detailed flowchart illustrating operation 740 illustratedin FIG. 7.

In operation 900, a function is derived from the gain parameters gp andgc of the PGF by using regression analysis. In operation 910, acoefficient of the derived function is adjusted according to a voicedlevel of the PGF. In operation 920, a gain parameter of the EF ispredicted using the coefficient-adjusted function. In operation 930, itis determined whether the predicted gain parameter falls outside apredetermined range. When it is determined that the predicted gainparameter falls outside the predetermined range in operation 930, thepredicted gain parameter is adjusted to a value within the predeterminedrange in operation 940. When it is determined that the predicted gainparameter does not fall outside the predetermined range in operation930, operation 950 is performed. In operation 950, an excitation signalis synthesized using the gain parameter predicted in operation 920 orthe gain parameter adjusted in operation 940. The synthesized excitationsignal is output as a reconstructed excitation signal of the EF inoperation 960.

Hereinafter, LSP parameter reconstruction (operation 750) illustrated inFIG. 7 will be described in detail. FIG. 10 is a detailed flowchartillustrating operation 750 illustrated in FIG. 7.

In operation 1000, spectrum parameters are generated by converting anLSP parameter of the PGF into a spectral domain. In operation 1010, afunction is derived from the generated spectrum parameters usingregression analysis. In operation 1020, a spectrum parameter of the EFis predicted using the derived function. In operation 1030, it isdetermined whether the predicted spectrum parameter falls outside apredetermined range. When it is determined that the predicted spectrumparameter falls outside the predetermined range in operation 1030, thepredicted spectrum parameter is adjusted to a value within thepredetermined range in operation 1040. When it is determined that thepredicted spectrum parameter does not fall outside the predeterminedrange in operation 1030, operation 1050 is performed. In operation 1050,the spectrum parameter predicted in operation 1020 or the spectrumparameter adjusted in operation 1040 is converted into an LSP parameter.In operation 1060, the converted LSP parameter is output as areconstructed LSP parameter of the EF.

Hereinafter, an audio decoding method using frame error concealmentaccording to an embodiment of the present general inventive concept willbe described with reference to FIG. 11. FIG. 11 is a flowchartillustrating an audio decoding method using a frame error concealmentmethod according to an embodiment of the present general inventiveconcept.

In operation 1100, it is determined whether a frame of a bitstream hasan error. If it is determined that no error is generated in operation1100, a spectrum parameter of a GF having no error is decoded inoperation 1105. In operation 1110, an audio signal of the GF isreconstructed using the decoded spectrum parameter. If it is determinedthat an error is generated in operation 1100, signal characteristics areanalyzed based on information about a PGF in operation 1120. Aregression analysis method to conceal the error is set based on theanalyzed signal characteristics in operation 1130. In operation 1140, aspectrum parameter of the EF is reconstructed using spectrum parametersof the PGF using the set regression analysis method. In operation 1150,an audio signal of the EF is reconstructed using the reconstructedspectrum parameter.

Hereinafter, signal characteristic analysis (operation 1120) andconcealment method setting (operation 1130) illustrated in FIG. 11 willbe described in detail with reference to FIG. 12. FIG. 12 is a detailedflowchart illustrating operation 1120 and operation 1130 illustrated inFIG. 11.

In operation 1200, signal characteristics are analyzed based oninformation about the PGF. In operation 1210, it is determined whetherthe current signal is static based on the analyzed signalcharacteristics. If it is determined that the current signal is staticin operation 1210, setting is performed to reconstruct a parameter ofthe EF using linear regression analysis in operation 1220 and regressionanalysis can be performed by referring to K PGFs in operation 1230. Ifit is determined that the current signal is not static in operation1210, setting is performed to reconstruct the parameter of the EF usingnon-linear regression analysis in operation 1240 and regression analysiscan be performed by referring to L PGFs in operation 1250. Here, K maybe an integer that is greater than L. Since a static audio signal hashigh correlation with its previous signal, it is desirable to refer to alonger interval of the previous signal than with a dynamic audio signalin order to obtain an accurate and natural signal. In contrast, sincethe dynamic audio signal has low correlation with its previous signal,it is desirable to refer to a shorter interval of the previous signalthan with the static audio signal.

Hereinafter, an audio decoding method using frame error concealmentaccording to another embodiment of the present general inventive conceptwill be described with reference to FIG. 13. FIG. 13 is a flowchartillustrating an audio decoding method using a frame error concealmentmethod according to another embodiment of the present general inventiveconcept.

In operation 1300, it is determined whether a frame of a bitstream hasan error. If it is determined that no error is generated in operation1300, spectrum parameters of a GF having no error are reconstructed inoperation 1310. In operation 1320, an audio signal of the GF isreconstructed using the reconstructed spectrum parameters.

If it is determined that an error is generated in operation 1300, theposition of the error in the frame is detected in operation 1330. Thisis because when the bitstream has a layered structure, a PGL thatprecedes the position of the error can be decoded normally. Thus,spectrum parameters of the PGL that precedes the position of the errorin the frame are decoded in operation 1340. In operation 1350, signalcharacteristics are analyzed based on information about the PGF andinformation about the PGL. In operation 1360, a regression analysismethod to reconstruct spectrum parameters of an error layer includingthe position of the error and its following layer is set based on theanalyzed signal characteristics. In operation 1370, the spectrumparameters of the error layer and its following layer are reconstructedusing the spectrum parameters of the PGF and the spectrum parameters ofthe PGL using the set regression analysis method. In operation 1370, anaudio signal of the EF is reconstructed using the spectrum parameters ofthe PGL, which are decoded in operation 1340, and the spectrumparameters of the error layer and its following layer, which are decodedin operation 1370.

As described above, in a decoding method and apparatus using frame errorconcealment according to embodiments of the present general inventiveconcept, signal characteristics of an EF are analyzed, an optimizedregression analysis method to reconstruct an EF is set based on theanalyzed signal characteristics, and parameters of the EF arereconstructed using the set regression analysis method, therebyaccurately reconstructing the EF and thus minimizing sound qualitydegradation caused by a frame error.

Moreover, in a decoding method and apparatus using frame errorconcealment according to another embodiment of the present generalinventive concept, when a bitstream has a layered structure, instead ofreconstructing the entire EF according to frame error concealment,layers preceding a layer having an error are reconstructed normally andthe layer having an error and its following layer are reconstructedaccording to frame error concealment by referring to reconstructedparameters of a PGL, thereby accurately reconstructing the EF and thusminimizing sound quality degradation caused by the frame error.

The present general inventive concept can also be embodied as code thatis readable by a computer on a computer-readable medium. Thecomputer-readable medium includes all kinds of recording devices storingdata that is readable by a computer system. Examples of thecomputer-readable medium include read-only memory (ROM), random accessmemory (RAM), CD-ROMs, magnetic tapes, floppy disks, optical datastorage devices, and carrier waves such as data transmission over theInternet. The computer-readable medium can also be distributed overnetwork coupled computer systems so that the computer readable code isstored and executed in a distributed fashion. Also, functional programs,code, and code segments to accomplish the present general inventiveconcept can be easily construed by programmers skilled in the art towhich the general inventive concept pertains.

As discussed above, in a frame error concealment method and apparatusaccording to various embodiments of the present general inventiveconcept, a frame error is concealed according to a concealment methodthat is set based on signal characteristics, thereby accuratelyreconstructing an error frame.

Moreover, when a bitstream has a layered structure, a previous goodlayer preceding an error layer having an error is decoded normally andthe error layer and its following layer are reconstructed by referringto decoding results of a previous good frame and the previous goodlayer, thereby accurately reconstructing the error layer.

Furthermore, in a frame error concealment method and apparatus accordingto various embodiments of the present general inventive concept, since aframe error is concealed using regression analysis, an error frame or anerror layer can be accurately predicted by finely considering changes ofa previous good frame and a previous good layer.

Additionally, in a decoding method and apparatus, an error frame isconcealed using a frame error concealment method and apparatus accordingto various embodiments of the present general inventive concept, therebyminimizing sound quality degradation caused by a frame error.

Although a few embodiments of the present general inventive concept havebeen illustrated and described, it will be appreciated by those skilledin the art that changes may be made in these embodiments withoutdeparting from the principles and spirit of the general inventiveconcept, the scope of which is defined in the appended claims and theirequivalents.

1. A frame error concealment method, comprising: setting a concealmentmethod to conceal an error based on one or more signal characteristicsof an error frame having the error; and concealing the error using theset concealment method.
 2. The method of claim 1, wherein the settingoperation comprises setting a regression analysis method to conceal theerror based on the one or more signal characteristics.
 3. The method ofclaim 2, wherein the setting operation further comprises: analyzing theone or more signal characteristics; and setting the regression analysismethod based on the one or more analyzed signal characteristics.
 4. Themethod of claim 3, wherein the analyzing operation comprises analyzingthe one or more signal characteristics based on information about aprevious good frame.
 5. The method of claim 4, wherein the analyzingoperation comprises analyzing the one or more signal characteristicsbased on at least one of class information and energy information of avoice component of the previous good frame.
 6. The method of claim 4,wherein the analyzing comprises analyzing the one or more signalcharacteristics based on at least one of attack signal information,window information, and energy information of the previous good frame.7. The method of claim 2, wherein the setting the regression analysismethod operation comprises selecting at least one of a linear regressionanalysis and a non-linear regression analysis as the concealment methodbased on the one or more signal characteristics.
 8. The method of claim3, wherein the setting the regression analysis method operationcomprises setting a number of previous good frames to be referred to andto conceal the error using the set regression analysis method based onthe one or more signal characteristics.
 9. The method of claim 3,wherein the setting the regression method operation comprises setting aninterval to extract one or more parameters of a previous good frame tobe referred to and to conceal the error using the set regressionanalysis method based on the one or more signal characteristics.
 10. Themethod of claim 2, wherein the concealing operation comprises predictinga parameter of the error frame from one or more parameters of theprevious good frame using the set regression analysis method.
 11. Themethod of claim 10, wherein the concealing operation further comprises:deriving a regression analysis function to predict from the one or moreparameters of the previous good frame using the set regression analysismethod; and predicting the parameter of the error frame using thederived regression analysis function.
 12. The method of claim 11,wherein the concealing operation further comprises: adjusting thepredicted parameter to a value included in a predetermined range whenthe predicted parameter falls outside the predetermined range.
 13. Themethod of claim 11, wherein the setting operation comprises setting anadjustment function to adjust the predicted parameter based on the oneor more signal characteristics and the predicting the parameteroperation comprises adjusting a coefficient of the derived functionusing the set adjustment function and predicting the parameter of theerror frame using the coefficient-adjusted function.
 14. The method ofclaim 13, wherein the function whose coefficient is adjusted using theset adjustment function is a function to predict a parameter associatedwith energy information of the error frame.
 15. The method of claim 1,further comprising: detecting the error frame from a bitstream.
 16. Aframe error concealment method, comprising: setting a concealment methodto conceal an error for an error layer including the position of theerror and its following layer in an error frame having the error basedon one or more signal characteristics of the error frame; and concealingthe error using the set concealment method.
 17. The method of claim 16,wherein the setting operation comprises: analyzing the one or moresignal characteristics based on information about a previous good frameand information about a previous layer preceding the error layer; andsetting the concealment method to conceal the error based on the one ormore analyzed signal characteristics.
 18. The method of claim 16,wherein the concealing operation comprises predicting one or moreparameters of the error layer and its following layer from one or moreparameters of the previous good frame and the previous layer using theset concealment method.
 19. A decoding method, comprising: detecting anerror frame having an error from a bitstream; decoding a frame having noerror in the bitstream; setting a concealment method to conceal theerror based on one or more signal characteristics of the error frame;and concealing the error using the set concealment method.
 20. Adecoding method, comprising: detecting an error frame having an errorfrom a bitstream and the position of the error in the error frame;decoding a frame having no error in the bitstream and a previous layerpreceding an error layer including the position of the error in theerror frame; setting a concealment method to conceal the error based onone or more signal characteristics of the error frame; and concealingthe error using the set concealment method.
 21. A frame errorconcealment apparatus, comprising: a concealment method setting unit toset a concealment method to conceal an error based on one or more signalcharacteristics of an error frame having the error; and an errorconcealment unit to conceal the error using the set concealment method.22. The apparatus of claim 21, wherein the concealment method settingunit sets a regression analysis method to conceal the error based on theone or more signal characteristics and the error concealment unitconceals the error using the set concealment method.
 23. The apparatusof claim 22, wherein the concealment method setting unit selects atleast one of a linear regression analysis and a non-linear regressionanalysis as the concealment method based on the one or more signalcharacteristics.
 24. The frame error concealment apparatus of claim 22,wherein the concealment method setting unit sets a number of previousgood frames to be referred to and to conceal the error using the setregression analysis method based on the one ore more signalcharacteristics.
 25. The apparatus of claim 22, wherein the concealmentmethod setting unit sets an interval to extract one or more parametersof a previous good frame to be referred to and to conceal the errorusing the set regression analysis method based on the one or more signalcharacteristics.